Within \(\mathcal {G}\) , we assume that some shots describe key
events in the movie (thick circles in Figure REF ) while
all shots have a sentiment (positive or negative), whose
intensity is denoted by a score (shades of green/red in
Figure REF ). We propose an algorithm for
traversing \(\mathcal {G}\)  and selecting sequences of trailer shots. In
the following, we first describe this algorithm
(Section REF ) and then discuss how the
graph \(\mathcal {G}\)  is learned and key events are detected via TP
identification [1]}
(Section REF ). Finally, we also explain how
shot-based sentiment scores are predicted
(Section REF ).
